Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional method in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.

翻译：基于预测的主动感知通过预先感知未知环境中的不确定性，有望提升机器人导航效率与安全性。现有3D形状预测方法对局部观测存在隐性假设，因而无法应用于实际场景规划，且未考虑下一最佳视角规划中的控制代价。本文提出Pred-NBV——一种由PoinTr-C（在ShapeNet数据集上训练的增强型3D预测模型）与基于信息量与控制代价的下一最佳视角方法组成的真实物体形状重建方案，以解决上述问题。在AirSim仿真器中，Pred-NBV相较传统方法在物体覆盖率上提升25.46%；即便面对搭载于DJI M600 Pro无人机上的Velodyne 3D激光雷达采集的真实数据，其形状补全性能仍优于当前最先进的补全模型PoinTr。